Identifying Selected Genes with Quaint
Emily Josephs
Last updated: 2020-03-20
Checks: 5 2
Knit directory: Blancetal-1/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 450bded | em | 2020-03-19 | more plots |
| html | 450bded | em | 2020-03-19 | more plots |
| Rmd | 626d202 | em | 2020-03-19 | num sig genes |
| Rmd | 2688954 | em | 2020-03-18 | merge conflict ugh |
| Rmd | 8c52f3c | Em | 2020-03-17 | stuff |
| Rmd | dec95d3 | Em | 2020-03-04 | pc figures |
| html | dec95d3 | Em | 2020-03-04 | pc figures |
| Rmd | 993e4b7 | Em | 2020-03-03 | mroe stuff |
| html | 993e4b7 | Em | 2020-03-03 | mroe stuff |
| Rmd | a2e0aec | Em | 2020-02-27 | drought gene analysis |
| html | a2e0aec | Em | 2020-02-27 | drought gene analysis |
| Rmd | c018f1d | Em | 2020-02-26 | added drought info |
| Rmd | 227a4b6 | Em | 2020-02-26 | added figure |
| html | 227a4b6 | Em | 2020-02-26 | added figure |
| Rmd | a597c91 | Em | 2020-02-26 | stuff |
| html | a597c91 | Em | 2020-02-26 | stuff |
Intro
I’m rerunning the code to identify selected genes with the Quaint package
### function for testing all genes
testAllGenes <- function(myTissue){
# Read in mean-centered expression values
df1 <- read.table(paste("../data/Mean_centered_expression/",myTissue,".txt",sep=""))
geneNames = names(df1)
# Read in tissue specific kinship matrix
myF <- read.table(paste('../data/Kinship_matrices/F_',myTissue,'.txt',sep=""))
## Get Eigen Values and Vectors
myE <- eigen(myF)
E_vectors <- myE$vectors
E_values <- myE$values
## Testing for selection on first 5 PCs
myM = 1:5
## Using the last 1/2 of PCs to estimate Va
myL = 6:dim(myF)[1]
## test for selection on each gene
allGeneOutput <- sapply(1:ncol(df1), function(x){
myQpc = calcQpc(myZ = df1[,x], myU = E_vectors, myLambdas = E_values, myL = myL, myM = myM)
return(c(geneNames[x],myQpc))
})
return(allGeneOutput)
}
###run on all genes
alltissues = c('GRoot',"Kern","LMAD26","LMAN26", "L3Tip","GShoot","L3Base")
alltissueresults = lapply(alltissues, testAllGenes)
names(alltissueresults) <- alltissues
##look at sig results
sigresults = lapply(1:length(alltissues), function(i){
# extract the pvalues
pvals = matrix(unlist(alltissueresults[[i]][5,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to PCs
##look at pvalue distributions
#par(mfrow=c(3,2), mar=c(4,4,1,1))
#sapply(1:5, function(x){
#hist(pvals[,x], border="white", col = "darkgray", main="", breaks = 20, xlab = paste('PC ',x,' ',alltissues[i],sep=""))
#})
## test fpr significance with the fdr
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')}))
## how many are significant?
numsig <- apply(pfdr, 2, function(x){sum(x<0.1)})
numsig
})
###make a big image of how many sig results we have
sigTable = as.data.frame(matrix(unlist(sigresults), ncol=5, byrow=T))
names(sigTable) = c('PC1','PC2','PC3','PC4','PC5')
sigTable$nicename = c('Germinating root', 'Kernel','Adult Leaf Day', 'Adult Leaf Night', '3rd leaf tip', 'Germinating shoot','3rd leaf base')
sigLong = tidyr::gather(sigTable, 'variable','value', -nicename)
sigLong[sigLong == 0] <- NA
pl <- ggplot(data=sigLong,aes(x=variable,y=nicename)) +
geom_tile(aes(fill=value),color='black') + scale_fill_gradient(low = 'lightyellow', high = "#CC79A7", guide = FALSE, na.value = "white") + theme_bw() + xlab("\n") + ylab("Tissue") +
theme(axis.ticks=element_blank(),panel.border=element_blank(),panel.grid.major = element_blank(), axis.text.y = element_text(size=10,angle=0), axis.title.y = element_text(size=16),axis.title.x = element_text(size=16),axis.text.x = element_text(angle = 0, hjust = 0.5,size=14)) + geom_text(aes(label=value),colour="grey15",size=5.5)
plWarning: Removed 20 rows containing missing values (geom_text).
###how many genes total?
sum(sigLong$value, na.rm=T) ##number of genes, but could have same gene for multiple tissues or PCs.[1] 67#unique genes
siggenes = unlist(lapply(1:length(alltissues), function(i){
# extract the pvalues
pvals = matrix(unlist(alltissueresults[[i]][5,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to PCs
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')})) ## test fpr significance with the fdr
pfdr$numsig <- apply(pfdr, 1, function(x){sum(x<0.1)})
pfdr$genes = unlist(alltissueresults[[i]][1,])
return(dplyr::filter(pfdr, numsig>0)$genes)
}))
length(unique(siggenes)) ##number of unique genes[1] 60###save output for other analyses
save(alltissueresults, file = "../data/quaint-results.rda")Make some individual gene plots for leaf expression 26
##what gene to plot
pvals = matrix(unlist(alltissueresults[['LMAD26']][5,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')}))
pfdr$index = 1:nrow(pfdr)
##the sig LMAD26 one on PC 1
sigPC1 = dplyr::filter(pfdr, X1 < 0.1)
### function for making plots
makeGenePlot <- function(tissue, geneIndex, pc){
## read in population data
pop <- read.csv("../data/FlintGarciaTableS1_fixednew.csv", stringsAsFactors = F)
## read in expression data
exp <- read.table(paste("../data/Mean_centered_expression/",tissue,".txt", sep=""), stringsAsFactors = F)
expgene = data.frame(Inbred = row.names(exp), geneexp = exp[,geneIndex], stringsAsFactors = F)
## merge data together
pop_dat <- dplyr::inner_join(pop, expgene, by= "Inbred")
#get the eigenvalues
myF <- read.table(paste('../data/Kinship_matrices/F_',tissue,'.txt', sep=""))
myE = eigen(myF)
myPC = data.frame(pc = myE$vectors[,pc], stringsAsFactors = F)
pop_dat <- dplyr::bind_cols(pop_dat, myPC)
lambda <- myE$values[pc]
##calculate the CIs
generesults = alltissueresults[[tissue]][,geneIndex]
myVaest = var0(generesults$cml)
myCI = 1.96*sqrt(myVaest*lambda)
##gene name
geneName = names(exp)[geneIndex]
lR <- lm(pop_dat$geneexp ~ pop_dat$pc)
coeff <- lR$coefficients[[2]]
if(tissue=="LMAD26"){mylab = c("mixed", "non-stiff stalk", "popcorn", "stiff stalk", "tropical")} else{mylab = c("mixed", "non-stiff stalk", "popcorn", "stiff stalk", "sweet", "tropical")} ##no sweets in LMAD26
col <- c('#E69F00', '#56B4E9', "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
pl1 <- ggplot(data=pop_dat, aes(x = pc, y= geneexp , color=Subpopulation)) + scale_colour_manual(values = col, labels=mylab) + xlab(paste("PC",pc)) + ylab("Expression") + theme_bw() + theme(panel.grid.major = element_blank(), text = element_text(size=15), panel.grid.minor = element_blank(), axis.title.y = element_text(size=18), axis.title.x = element_text(size=18), legend.position = "right", legend.title = element_text(size = 12), legend.text = element_text(size = 20), plot.title = element_text(hjust = 0.5)) + geom_point(size = 3.5) + geom_abline(slope = myCI, linetype = 2, col = '#56B4E9', size=1.2) + geom_abline(slope = coeff, size = 1.5)+ geom_abline(slope = -myCI, linetype = 2, col = '#56B4E9', size=1.2)
return(c(pl1))
}
myplot = makeGenePlot(tissue = 'LMAD26',geneIndex = sigPC1$index, pc = 1)
myplot$data
Inbred State.Country NSS SS TS Pop.A Sweet.A
1 4226 Illinois 0.917 0.071 0.012 0 0
2 33-16 Indiana 0.972 0.014 0.014 0 0
3 A214NGoodman-Buckler South Africa 0.017 0.762 0.221 0 0
4 A441-5 South Africa 0.531 0.005 0.464 0 0
5 A6 Pioneer 0.03 0.003 0.967 0 0
6 Ab28A Alabama 0.776 0.002 0.222 0 0
7 B46 Iowa 0.784 0.214 0.002 0 0
8 B52 Iowa 0.985 0.012 0.003 0 0
9 B64 Iowa 0.002 0.988 0.01 0 0
10 B73 Iowa 0.001 0.999 0.001 0 0
11 B77 Iowa 0.915 0.083 0.002 0 0
12 B84 Iowa 0.003 0.996 0.001 0 0
13 Ci7Goodman-Buckler USDA 0.994 0.004 0.001 0 0
14 CI21E USDA 0.866 0.119 0.014 0 0
15 CI28AGoodman-Buckler USDA 0.749 0.004 0.247 0 0
16 CI31A USDA 0.994 0.002 0.004 0 0
17 CI64 USDA 0.986 0.012 0.002 0 0
18 CI66 USDA 0.935 0.011 0.054 0 0
19 Ci91BGoodman-Buckler USDA 0.997 0.002 0.002 0 0
20 CML10 Mexico 0.002 0.002 0.995 0 0
21 CML103 Mexico 0.005 0.003 0.992 0 0
22 CML108 Mexico 0.099 0.002 0.899 0 0
23 CML11 Mexico 0.002 0.003 0.995 0 0
24 CML154Q Mexico 0.005 0.002 0.993 0 0
25 CML157Q Mexico 0.018 0.004 0.978 0 0
26 CML158Q Mexico 0.018 0.01 0.971 0 0
27 CML218 Mexico 0.301 0.01 0.689 0 0
28 CML220 Mexico 0.053 0.059 0.887 0 0
29 CML228 Mexico 0.002 0.003 0.995 0 0
30 CML261 Mexico 0.004 0.016 0.981 0 0
31 CML277 Mexico 0.004 0.001 0.995 0 0
32 CML314 Mexico 0.002 0.003 0.995 0 0
33 CML322 Mexico 0.005 0.01 0.985 0 0
34 CML323 Mexico 0.34 0.007 0.653 0 0
35 CML331 Mexico 0.003 0.002 0.994 0 0
36 CML333 Mexico 0.029 0.013 0.959 0 0
37 CML38 Mexico 0.002 0.001 0.997 0 0
38 CML69 Mexico 0.028 0.006 0.966 0 0
39 CML77 Mexico 0.302 0.005 0.693 0 0
40 CML91 Mexico 0.218 0.015 0.767 0 0
41 D940Y South Africa 0.614 0.004 0.382 0 0
42 DE811 Delaware 0.218 0.779 0.003 0 0
43 F6 Florida 0.745 0.005 0.25 0 0
44 GA209 Georgia 0.979 0.004 0.017 0 0
45 GT112 Georgia 0.839 0.008 0.153 0 0
46 Hi27Goodman-Buckler Hawaii 0.019 0.207 0.774 0 0
47 ILLHy Illinois 0.898 0.079 0.024 0 0
48 I137TN South Africa 0.539 0.002 0.459 0 0
49 K4 Kansas 0.873 0.11 0.017 0 0
50 K64 Kansas 0.965 0.033 0.002 0 0
51 Ki11 Thailand 0.017 0.003 0.98 0 0
52 Ki14 Thailand 0.003 0.003 0.994 0 0
53 Ki21 Thailand 0.393 0.001 0.606 0 0
54 KI3 Thailand 0.002 0.001 0.997 0 0
55 Ki44 Thailand 0.018 0.001 0.981 0 0
56 Ky21 Kentucky 0.859 0.008 0.133 0 0
57 KY228 Kentucky 0.709 0.134 0.157 0 0
58 L578 Louisiana 0.297 0.007 0.696 0 0
59 M162W South Africa 0.996 0.002 0.002 0 0
60 M37W South Africa 0.404 0.012 0.584 0 0
61 Mo18W Missouri 0.307 0.011 0.683 0 0
62 Mp339 Mississippi 0.641 0.102 0.258 0 0
63 N28Ht Nebraska 0.447 0.551 0.002 0 0
64 NC222 North Carolina 0.874 0.004 0.122 0 0
65 NC230 North Carolina 0.994 0.004 0.002 0 0
66 NC232 North Carolina 0.839 0.068 0.094 0 0
67 NC236 North Carolina 0.974 0.023 0.002 0 0
68 NC238 North Carolina 0.989 0.004 0.007 0 0
69 NC250 North Carolina 0.057 0.941 0.002 0 0
70 NC258 North Carolina 0.997 0.001 0.001 0 0
71 NC262 North Carolina 0.991 0.007 0.001 0 0
72 NC296 North Carolina 0.001 0.002 0.997 0 0
73 NC300 North Carolina 0.003 0.002 0.995 0 0
74 NC302 North Carolina 0.003 0.002 0.996 0 0
75 NC304 North Carolina 0.002 0.001 0.997 0 0
76 NC306 North Carolina 0.001 0.998 0.001 0 0
77 NC314 North Carolina 0.001 0.998 0.001 0 0
78 NC318 North Carolina 0.032 0.002 0.966 0 0
79 NC320 North Carolina 0.005 0.004 0.991 0 0
80 NC326 North Carolina 0.001 0.998 0.001 0 0
81 NC33 North Carolina 0.862 0.129 0.01 0 0
82 NC336 North Carolina 0.001 0.002 0.997 0 0
83 NC338 North Carolina 0.003 0.002 0.995 0 0
84 NC342 North Carolina 0.994 0.005 0.001 0 0
85 NC346 North Carolina 0.001 0.002 0.997 0 0
86 NC348 North Carolina 0.001 0.001 0.997 0 0
87 NC350 North Carolina 0.002 0.003 0.995 0 0
88 NC352 North Carolina 0.001 0.003 0.996 0 0
89 NC354 North Carolina 0.001 0.01 0.989 0 0
90 NC356 North Carolina 0.031 0.001 0.968 0 0
91 NC360 North Carolina 0.475 0.001 0.523 0 0
92 NC366 North Carolina 0.467 0.018 0.514 0 0
93 OH7B Ohio 0.967 0.031 0.002 0 0
94 Os420 Iowa 0.983 0.016 0.001 0 0
95 Pa880 Pennsylvania 0.993 0.002 0.005 0 0
96 R4 Illinois 0.979 0.019 0.003 0 0
97 SC213R South Carolina 0.579 0.002 0.419 0 0
98 SC357 South Carolina 0.622 0.003 0.375 0 0
99 SC55 South Carolina 0.626 0.046 0.328 0 0
100 Sg1533 Indiana 0 0 0 1 0
101 T232 Tennessee 0.459 0.14 0.402 0 0
102 T234 Tennessee 0.985 0.003 0.012 0 0
103 Tx303 Texas 0.36 0.065 0.575 0 0
104 Tzi11 Nigeria 0.184 0.002 0.814 0 0
105 Tzi16 Nigeria 0.351 0.248 0.401 0 0
106 Tzi18 Nigeria 0.003 0.002 0.995 0 0
107 Tzi25 Nigeria 0.086 0.305 0.608 0 0
108 Tzi9 Nigeria 0.011 0.008 0.982 0 0
109 U267Y South Africa 0.595 0.002 0.403 0 0
Subpopulation geneexp pc
1 nss -49.9549547 -0.0938709603
2 nss 8.9132153 -0.0550962753
3 mixed -40.3115747 -0.0678989812
4 mixed -52.0427547 -0.0369337467
5 ts -15.5525947 0.0812371354
6 mixed -20.9265047 -0.0631156865
7 mixed -47.1941047 -0.0986465908
8 nss 3.3097553 -0.1027919906
9 ss -30.0616547 -0.0719570913
10 ss 6.4533853 -0.0865490906
11 nss -33.0792547 -0.0865742004
12 ss -32.1198647 -0.0905144082
13 nss -23.3942047 -0.0716266562
14 nss 2.2324053 -0.0462581332
15 mixed -45.6441247 -0.0503897309
16 nss -46.5271547 -0.0551176190
17 nss -8.7744347 -0.0447634477
18 nss -60.5269847 0.1003371843
19 nss 12.0652453 0.0248547102
20 ts -18.6605547 0.0094390285
21 ts -45.7237847 0.1101976092
22 ts -45.1415347 0.0560112182
23 ts -40.4683147 0.0583932643
24 ts -21.8132547 0.0570619653
25 ts -35.8208647 0.0472942311
26 ts -21.0618047 0.0496432732
27 mixed -7.7519547 0.0558168909
28 ts -28.7004247 0.0639809502
29 ts -18.4412347 0.0741184401
30 ts -45.9786947 0.0757726109
31 ts 14.0078553 0.0673963710
32 ts -34.3443647 0.0140729719
33 ts -30.0756547 0.0736874708
34 mixed 0.7359853 0.0302806702
35 ts -1.0734247 0.0570165806
36 ts -13.9604447 0.0656795723
37 ts -22.1994247 0.0613023203
38 ts -34.6484047 0.0419366271
39 mixed -19.3255547 -0.0882075431
40 mixed -17.6953447 -0.1031565469
41 mixed -26.6395147 -0.0665942429
42 mixed -36.3002647 -0.1048684607
43 mixed -11.7400947 -0.0511226797
44 nss -33.8830247 -0.0510870772
45 nss -32.7262747 -0.0388735166
46 mixed -35.5182647 0.0161509586
47 nss -33.8155747 -0.0335926736
48 mixed 1.2795253 -0.1076271239
49 nss -32.5306947 -0.0686867598
50 nss -8.1507847 -0.0775903762
51 ts -14.2157647 0.0785666199
52 ts 38.3104053 -0.0750862189
53 mixed 53.7370153 0.0485080730
54 ts 14.8903153 0.0597131622
55 ts -11.7271047 0.0076159008
56 nss -14.9723947 0.0478964230
57 mixed -55.7696247 -0.0519818961
58 mixed -61.0731247 -0.0186206752
59 nss -18.6659747 -0.0408010958
60 mixed -42.5553947 -0.0214810656
61 mixed -10.9834047 -0.0098343847
62 mixed -15.1143647 -0.0146143402
63 mixed -34.4410247 -0.0841579516
64 nss 57.8117653 -0.0658968363
65 nss -53.3154747 -0.0615723334
66 nss -8.5853047 -0.0378761186
67 nss -22.7284547 -0.0592827982
68 nss 0.2065253 -0.0487056113
69 ss -55.8129647 -0.0836393247
70 nss -46.7317047 -0.1519298297
71 nss -17.5211547 -0.1347691003
72 ts 166.3686653 0.2682643362
73 ts -27.6443647 0.1116537960
74 ts -32.5350447 0.1901004468
75 ts 132.0767553 0.1808828622
76 ss -40.9382547 -0.0874644056
77 ss -16.6449447 -0.1121934112
78 ts -18.1200347 -0.0330000624
79 ts 1.0370753 -0.0004556836
80 ss -26.8363547 -0.0930784384
81 nss -23.2427847 -0.0597927320
82 ts 336.7135553 0.2701029887
83 ts 185.1931153 0.2121535362
84 nss 12.4931853 -0.1400400274
85 ts 213.2671253 0.2683212353
86 ts 200.9309853 0.2197777118
87 ts 170.9524953 0.1728926473
88 ts 173.6878153 0.2796897494
89 ts 244.9725453 0.2099533996
90 ts 290.7957453 0.1302855771
91 mixed 4.6222653 -0.0339996456
92 mixed -14.7472647 -0.0344065881
93 nss -28.8124947 -0.0875199560
94 nss -19.0338247 -0.0687770508
95 nss -21.0966747 -0.0706156325
96 nss -23.7149947 -0.0964544175
97 mixed -16.1155447 -0.0728479342
98 mixed 2.8136153 -0.0314686133
99 mixed 2.3707053 -0.0050464612
100 popcorn -44.6042547 -0.0702118823
101 mixed -39.6816247 -0.0425517185
102 nss -11.1215747 -0.0549730545
103 mixed -19.2688547 -0.0043306534
104 ts -51.7763447 0.0345413022
105 mixed -7.2472647 -0.0098647498
106 ts -11.5642747 0.0538102807
107 mixed -47.0380547 0.0226016434
108 ts -34.4923347 0.0395705068
109 mixed 6.9199253 -0.0460731877
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$coordinates
<ggproto object: Class CoordCartesian, Coord, gg>
aspect: function
backtransform_range: function
clip: on
default: TRUE
distance: function
expand: TRUE
is_free: function
is_linear: function
labels: function
limits: list
modify_scales: function
range: function
render_axis_h: function
render_axis_v: function
render_bg: function
render_fg: function
setup_data: function
setup_layout: function
setup_panel_guides: function
setup_panel_params: function
setup_params: function
train_panel_guides: function
transform: function
super: <ggproto object: Class CoordCartesian, Coord, gg>
$facet
<ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
$plot_env
<environment: 0x1a4bb088>
$labels
$labels$y
[1] "Expression"
$labels$x
[1] "PC 1"
$labels$colour
[1] "Subpopulation"
$labels$intercept
[1] "intercept"
$labels$slope
[1] "slope"alltissueresults[['LMAD26']][1,sigPC1$index]##get gene name[[1]]
[1] "GRMZM2G152686"#make a plot for PC2
sigPC2 = dplyr::filter(pfdr, X2 < 0.1)
pc2plot = makeGenePlot(tissue="LMAD26",geneIndex = sigPC2$index[2], pc=2)
pc2plot$data
Inbred State.Country NSS SS TS Pop.A Sweet.A
1 4226 Illinois 0.917 0.071 0.012 0 0
2 33-16 Indiana 0.972 0.014 0.014 0 0
3 A214NGoodman-Buckler South Africa 0.017 0.762 0.221 0 0
4 A441-5 South Africa 0.531 0.005 0.464 0 0
5 A6 Pioneer 0.03 0.003 0.967 0 0
6 Ab28A Alabama 0.776 0.002 0.222 0 0
7 B46 Iowa 0.784 0.214 0.002 0 0
8 B52 Iowa 0.985 0.012 0.003 0 0
9 B64 Iowa 0.002 0.988 0.01 0 0
10 B73 Iowa 0.001 0.999 0.001 0 0
11 B77 Iowa 0.915 0.083 0.002 0 0
12 B84 Iowa 0.003 0.996 0.001 0 0
13 Ci7Goodman-Buckler USDA 0.994 0.004 0.001 0 0
14 CI21E USDA 0.866 0.119 0.014 0 0
15 CI28AGoodman-Buckler USDA 0.749 0.004 0.247 0 0
16 CI31A USDA 0.994 0.002 0.004 0 0
17 CI64 USDA 0.986 0.012 0.002 0 0
18 CI66 USDA 0.935 0.011 0.054 0 0
19 Ci91BGoodman-Buckler USDA 0.997 0.002 0.002 0 0
20 CML10 Mexico 0.002 0.002 0.995 0 0
21 CML103 Mexico 0.005 0.003 0.992 0 0
22 CML108 Mexico 0.099 0.002 0.899 0 0
23 CML11 Mexico 0.002 0.003 0.995 0 0
24 CML154Q Mexico 0.005 0.002 0.993 0 0
25 CML157Q Mexico 0.018 0.004 0.978 0 0
26 CML158Q Mexico 0.018 0.01 0.971 0 0
27 CML218 Mexico 0.301 0.01 0.689 0 0
28 CML220 Mexico 0.053 0.059 0.887 0 0
29 CML228 Mexico 0.002 0.003 0.995 0 0
30 CML261 Mexico 0.004 0.016 0.981 0 0
31 CML277 Mexico 0.004 0.001 0.995 0 0
32 CML314 Mexico 0.002 0.003 0.995 0 0
33 CML322 Mexico 0.005 0.01 0.985 0 0
34 CML323 Mexico 0.34 0.007 0.653 0 0
35 CML331 Mexico 0.003 0.002 0.994 0 0
36 CML333 Mexico 0.029 0.013 0.959 0 0
37 CML38 Mexico 0.002 0.001 0.997 0 0
38 CML69 Mexico 0.028 0.006 0.966 0 0
39 CML77 Mexico 0.302 0.005 0.693 0 0
40 CML91 Mexico 0.218 0.015 0.767 0 0
41 D940Y South Africa 0.614 0.004 0.382 0 0
42 DE811 Delaware 0.218 0.779 0.003 0 0
43 F6 Florida 0.745 0.005 0.25 0 0
44 GA209 Georgia 0.979 0.004 0.017 0 0
45 GT112 Georgia 0.839 0.008 0.153 0 0
46 Hi27Goodman-Buckler Hawaii 0.019 0.207 0.774 0 0
47 ILLHy Illinois 0.898 0.079 0.024 0 0
48 I137TN South Africa 0.539 0.002 0.459 0 0
49 K4 Kansas 0.873 0.11 0.017 0 0
50 K64 Kansas 0.965 0.033 0.002 0 0
51 Ki11 Thailand 0.017 0.003 0.98 0 0
52 Ki14 Thailand 0.003 0.003 0.994 0 0
53 Ki21 Thailand 0.393 0.001 0.606 0 0
54 KI3 Thailand 0.002 0.001 0.997 0 0
55 Ki44 Thailand 0.018 0.001 0.981 0 0
56 Ky21 Kentucky 0.859 0.008 0.133 0 0
57 KY228 Kentucky 0.709 0.134 0.157 0 0
58 L578 Louisiana 0.297 0.007 0.696 0 0
59 M162W South Africa 0.996 0.002 0.002 0 0
60 M37W South Africa 0.404 0.012 0.584 0 0
61 Mo18W Missouri 0.307 0.011 0.683 0 0
62 Mp339 Mississippi 0.641 0.102 0.258 0 0
63 N28Ht Nebraska 0.447 0.551 0.002 0 0
64 NC222 North Carolina 0.874 0.004 0.122 0 0
65 NC230 North Carolina 0.994 0.004 0.002 0 0
66 NC232 North Carolina 0.839 0.068 0.094 0 0
67 NC236 North Carolina 0.974 0.023 0.002 0 0
68 NC238 North Carolina 0.989 0.004 0.007 0 0
69 NC250 North Carolina 0.057 0.941 0.002 0 0
70 NC258 North Carolina 0.997 0.001 0.001 0 0
71 NC262 North Carolina 0.991 0.007 0.001 0 0
72 NC296 North Carolina 0.001 0.002 0.997 0 0
73 NC300 North Carolina 0.003 0.002 0.995 0 0
74 NC302 North Carolina 0.003 0.002 0.996 0 0
75 NC304 North Carolina 0.002 0.001 0.997 0 0
76 NC306 North Carolina 0.001 0.998 0.001 0 0
77 NC314 North Carolina 0.001 0.998 0.001 0 0
78 NC318 North Carolina 0.032 0.002 0.966 0 0
79 NC320 North Carolina 0.005 0.004 0.991 0 0
80 NC326 North Carolina 0.001 0.998 0.001 0 0
81 NC33 North Carolina 0.862 0.129 0.01 0 0
82 NC336 North Carolina 0.001 0.002 0.997 0 0
83 NC338 North Carolina 0.003 0.002 0.995 0 0
84 NC342 North Carolina 0.994 0.005 0.001 0 0
85 NC346 North Carolina 0.001 0.002 0.997 0 0
86 NC348 North Carolina 0.001 0.001 0.997 0 0
87 NC350 North Carolina 0.002 0.003 0.995 0 0
88 NC352 North Carolina 0.001 0.003 0.996 0 0
89 NC354 North Carolina 0.001 0.01 0.989 0 0
90 NC356 North Carolina 0.031 0.001 0.968 0 0
91 NC360 North Carolina 0.475 0.001 0.523 0 0
92 NC366 North Carolina 0.467 0.018 0.514 0 0
93 OH7B Ohio 0.967 0.031 0.002 0 0
94 Os420 Iowa 0.983 0.016 0.001 0 0
95 Pa880 Pennsylvania 0.993 0.002 0.005 0 0
96 R4 Illinois 0.979 0.019 0.003 0 0
97 SC213R South Carolina 0.579 0.002 0.419 0 0
98 SC357 South Carolina 0.622 0.003 0.375 0 0
99 SC55 South Carolina 0.626 0.046 0.328 0 0
100 Sg1533 Indiana 0 0 0 1 0
101 T232 Tennessee 0.459 0.14 0.402 0 0
102 T234 Tennessee 0.985 0.003 0.012 0 0
103 Tx303 Texas 0.36 0.065 0.575 0 0
104 Tzi11 Nigeria 0.184 0.002 0.814 0 0
105 Tzi16 Nigeria 0.351 0.248 0.401 0 0
106 Tzi18 Nigeria 0.003 0.002 0.995 0 0
107 Tzi25 Nigeria 0.086 0.305 0.608 0 0
108 Tzi9 Nigeria 0.011 0.008 0.982 0 0
109 U267Y South Africa 0.595 0.002 0.403 0 0
Subpopulation geneexp pc
1 nss -5.6713698 0.056586315
2 nss -24.6410798 0.037999653
3 mixed -11.0793998 -0.016132312
4 mixed -28.8433698 -0.003472471
5 ts 21.8338602 -0.099765319
6 mixed -17.2450798 0.026811641
7 mixed -23.0152798 0.036900418
8 nss -10.0064398 0.057633663
9 ss 9.5486602 -0.030147520
10 ss -30.8158798 0.011448763
11 nss -24.9678098 0.084234202
12 ss -23.6925198 0.031363185
13 nss -40.2046698 0.032086369
14 nss 2.6464102 0.005334486
15 mixed 53.3986802 0.039422828
16 nss -17.2770998 -0.020886517
17 nss 18.5994002 0.028567592
18 nss 7.8086502 -0.216329540
19 nss -22.6675798 -0.067662353
20 ts 10.0589202 -0.038492584
21 ts 1.4732302 -0.211390699
22 ts -2.5369998 -0.126921256
23 ts 2.2331402 -0.118483463
24 ts 58.6422002 -0.114542252
25 ts 27.4427902 -0.122989764
26 ts -3.2690398 -0.097934530
27 mixed -12.2174998 -0.119722509
28 ts -17.5660398 -0.123725831
29 ts 46.8341802 -0.098356194
30 ts 69.0258502 -0.133033303
31 ts 58.7936802 -0.155048312
32 ts 28.9045102 -0.065593594
33 ts 16.2504202 -0.158740283
34 mixed 20.6326802 -0.089593928
35 ts 36.1863902 -0.129955410
36 ts 13.8856902 -0.137799973
37 ts 84.7887202 -0.089933157
38 ts 14.8557202 -0.094841560
39 mixed -8.1773498 0.049925078
40 mixed -13.4145198 0.026813364
41 mixed -6.6712698 0.024764784
42 mixed -20.5277998 0.045481834
43 mixed 9.9725902 0.020809441
44 nss 2.9102302 0.018646834
45 nss 6.7944902 0.034505834
46 mixed 4.3441902 -0.048629677
47 nss -7.9232498 -0.008340533
48 mixed 20.1758202 0.040350147
49 nss 18.9336202 0.029424329
50 nss -2.1549598 -0.011269218
51 ts 43.0032102 -0.162671771
52 ts 22.9270002 -0.002635315
53 mixed -1.4868698 -0.113348069
54 ts -16.5824898 -0.109785828
55 ts 47.5227902 -0.032019138
56 nss 19.1768002 -0.083101964
57 mixed 50.1782702 0.040865959
58 mixed -11.9288898 0.020143748
59 nss 42.3144302 -0.035609875
60 mixed 15.8000102 -0.022428117
61 mixed -7.7549398 -0.045528272
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$layers
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geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[3]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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get_scales: function
has_scale: function
input: function
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scales: list
super: <ggproto object: Class ScalesList, gg>
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Aesthetic mapping:
* `x` -> `pc`
* `y` -> `geneexp`
* `colour` -> `Subpopulation`
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$coordinates
<ggproto object: Class CoordCartesian, Coord, gg>
aspect: function
backtransform_range: function
clip: on
default: TRUE
distance: function
expand: TRUE
is_free: function
is_linear: function
labels: function
limits: list
modify_scales: function
range: function
render_axis_h: function
render_axis_v: function
render_bg: function
render_fg: function
setup_data: function
setup_layout: function
setup_panel_guides: function
setup_panel_params: function
setup_params: function
train_panel_guides: function
transform: function
super: <ggproto object: Class CoordCartesian, Coord, gg>
$facet
<ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
$plot_env
<environment: 0x21d8fb30>
$labels
$labels$y
[1] "Expression"
$labels$x
[1] "PC 2"
$labels$colour
[1] "Subpopulation"
$labels$intercept
[1] "intercept"
$labels$slope
[1] "slope"##make a plot for PC3
sigPC3 = dplyr::filter(pfdr, X3 < 0.1)
pc3plot = makeGenePlot(tissue="LMAD26",geneIndex = sigPC3$index[3], pc=3)
pc3plot$data
Inbred State.Country NSS SS TS Pop.A Sweet.A
1 4226 Illinois 0.917 0.071 0.012 0 0
2 33-16 Indiana 0.972 0.014 0.014 0 0
3 A214NGoodman-Buckler South Africa 0.017 0.762 0.221 0 0
4 A441-5 South Africa 0.531 0.005 0.464 0 0
5 A6 Pioneer 0.03 0.003 0.967 0 0
6 Ab28A Alabama 0.776 0.002 0.222 0 0
7 B46 Iowa 0.784 0.214 0.002 0 0
8 B52 Iowa 0.985 0.012 0.003 0 0
9 B64 Iowa 0.002 0.988 0.01 0 0
10 B73 Iowa 0.001 0.999 0.001 0 0
11 B77 Iowa 0.915 0.083 0.002 0 0
12 B84 Iowa 0.003 0.996 0.001 0 0
13 Ci7Goodman-Buckler USDA 0.994 0.004 0.001 0 0
14 CI21E USDA 0.866 0.119 0.014 0 0
15 CI28AGoodman-Buckler USDA 0.749 0.004 0.247 0 0
16 CI31A USDA 0.994 0.002 0.004 0 0
17 CI64 USDA 0.986 0.012 0.002 0 0
18 CI66 USDA 0.935 0.011 0.054 0 0
19 Ci91BGoodman-Buckler USDA 0.997 0.002 0.002 0 0
20 CML10 Mexico 0.002 0.002 0.995 0 0
21 CML103 Mexico 0.005 0.003 0.992 0 0
22 CML108 Mexico 0.099 0.002 0.899 0 0
23 CML11 Mexico 0.002 0.003 0.995 0 0
24 CML154Q Mexico 0.005 0.002 0.993 0 0
25 CML157Q Mexico 0.018 0.004 0.978 0 0
26 CML158Q Mexico 0.018 0.01 0.971 0 0
27 CML218 Mexico 0.301 0.01 0.689 0 0
28 CML220 Mexico 0.053 0.059 0.887 0 0
29 CML228 Mexico 0.002 0.003 0.995 0 0
30 CML261 Mexico 0.004 0.016 0.981 0 0
31 CML277 Mexico 0.004 0.001 0.995 0 0
32 CML314 Mexico 0.002 0.003 0.995 0 0
33 CML322 Mexico 0.005 0.01 0.985 0 0
34 CML323 Mexico 0.34 0.007 0.653 0 0
35 CML331 Mexico 0.003 0.002 0.994 0 0
36 CML333 Mexico 0.029 0.013 0.959 0 0
37 CML38 Mexico 0.002 0.001 0.997 0 0
38 CML69 Mexico 0.028 0.006 0.966 0 0
39 CML77 Mexico 0.302 0.005 0.693 0 0
40 CML91 Mexico 0.218 0.015 0.767 0 0
41 D940Y South Africa 0.614 0.004 0.382 0 0
42 DE811 Delaware 0.218 0.779 0.003 0 0
43 F6 Florida 0.745 0.005 0.25 0 0
44 GA209 Georgia 0.979 0.004 0.017 0 0
45 GT112 Georgia 0.839 0.008 0.153 0 0
46 Hi27Goodman-Buckler Hawaii 0.019 0.207 0.774 0 0
47 ILLHy Illinois 0.898 0.079 0.024 0 0
48 I137TN South Africa 0.539 0.002 0.459 0 0
49 K4 Kansas 0.873 0.11 0.017 0 0
50 K64 Kansas 0.965 0.033 0.002 0 0
51 Ki11 Thailand 0.017 0.003 0.98 0 0
52 Ki14 Thailand 0.003 0.003 0.994 0 0
53 Ki21 Thailand 0.393 0.001 0.606 0 0
54 KI3 Thailand 0.002 0.001 0.997 0 0
55 Ki44 Thailand 0.018 0.001 0.981 0 0
56 Ky21 Kentucky 0.859 0.008 0.133 0 0
57 KY228 Kentucky 0.709 0.134 0.157 0 0
58 L578 Louisiana 0.297 0.007 0.696 0 0
59 M162W South Africa 0.996 0.002 0.002 0 0
60 M37W South Africa 0.404 0.012 0.584 0 0
61 Mo18W Missouri 0.307 0.011 0.683 0 0
62 Mp339 Mississippi 0.641 0.102 0.258 0 0
63 N28Ht Nebraska 0.447 0.551 0.002 0 0
64 NC222 North Carolina 0.874 0.004 0.122 0 0
65 NC230 North Carolina 0.994 0.004 0.002 0 0
66 NC232 North Carolina 0.839 0.068 0.094 0 0
67 NC236 North Carolina 0.974 0.023 0.002 0 0
68 NC238 North Carolina 0.989 0.004 0.007 0 0
69 NC250 North Carolina 0.057 0.941 0.002 0 0
70 NC258 North Carolina 0.997 0.001 0.001 0 0
71 NC262 North Carolina 0.991 0.007 0.001 0 0
72 NC296 North Carolina 0.001 0.002 0.997 0 0
73 NC300 North Carolina 0.003 0.002 0.995 0 0
74 NC302 North Carolina 0.003 0.002 0.996 0 0
75 NC304 North Carolina 0.002 0.001 0.997 0 0
76 NC306 North Carolina 0.001 0.998 0.001 0 0
77 NC314 North Carolina 0.001 0.998 0.001 0 0
78 NC318 North Carolina 0.032 0.002 0.966 0 0
79 NC320 North Carolina 0.005 0.004 0.991 0 0
80 NC326 North Carolina 0.001 0.998 0.001 0 0
81 NC33 North Carolina 0.862 0.129 0.01 0 0
82 NC336 North Carolina 0.001 0.002 0.997 0 0
83 NC338 North Carolina 0.003 0.002 0.995 0 0
84 NC342 North Carolina 0.994 0.005 0.001 0 0
85 NC346 North Carolina 0.001 0.002 0.997 0 0
86 NC348 North Carolina 0.001 0.001 0.997 0 0
87 NC350 North Carolina 0.002 0.003 0.995 0 0
88 NC352 North Carolina 0.001 0.003 0.996 0 0
89 NC354 North Carolina 0.001 0.01 0.989 0 0
90 NC356 North Carolina 0.031 0.001 0.968 0 0
91 NC360 North Carolina 0.475 0.001 0.523 0 0
92 NC366 North Carolina 0.467 0.018 0.514 0 0
93 OH7B Ohio 0.967 0.031 0.002 0 0
94 Os420 Iowa 0.983 0.016 0.001 0 0
95 Pa880 Pennsylvania 0.993 0.002 0.005 0 0
96 R4 Illinois 0.979 0.019 0.003 0 0
97 SC213R South Carolina 0.579 0.002 0.419 0 0
98 SC357 South Carolina 0.622 0.003 0.375 0 0
99 SC55 South Carolina 0.626 0.046 0.328 0 0
100 Sg1533 Indiana 0 0 0 1 0
101 T232 Tennessee 0.459 0.14 0.402 0 0
102 T234 Tennessee 0.985 0.003 0.012 0 0
103 Tx303 Texas 0.36 0.065 0.575 0 0
104 Tzi11 Nigeria 0.184 0.002 0.814 0 0
105 Tzi16 Nigeria 0.351 0.248 0.401 0 0
106 Tzi18 Nigeria 0.003 0.002 0.995 0 0
107 Tzi25 Nigeria 0.086 0.305 0.608 0 0
108 Tzi9 Nigeria 0.011 0.008 0.982 0 0
109 U267Y South Africa 0.595 0.002 0.403 0 0
Subpopulation geneexp pc
1 nss 8.90324427 0.0354466972
2 nss 14.54715427 -0.0266885974
3 mixed -184.17553573 -0.1582059761
4 mixed 4.78571427 0.0261634579
5 ts 70.22315427 0.0335648183
6 mixed 3.28589427 0.0666320417
7 mixed -196.64895573 -0.1022185568
8 nss -198.29868573 -0.0937212130
9 ss -192.63745573 -0.1582927823
10 ss -33.03222573 -0.2038463977
11 nss 53.58589427 0.0519060327
12 ss 9.45162427 -0.1535455539
13 nss 27.37960427 -0.0330940350
14 nss -0.67744573 -0.0133540812
15 mixed -23.86504573 0.0078045436
16 nss 18.15642427 0.0888294663
17 nss -7.17911573 0.0620095891
18 nss -26.98063573 0.0250870709
19 nss -200.25963573 0.0216236548
20 ts 98.89583427 0.0174821817
21 ts 44.18599427 0.0252408568
22 ts -3.24244573 0.0403792152
23 ts 27.09765427 0.0398397927
24 ts 70.54114427 0.0370590924
25 ts 52.60214427 0.0384468534
26 ts 147.53394427 0.0351517294
27 mixed 18.05477427 -0.0133205579
28 ts 67.07571427 0.0344308437
29 ts 0.48855427 0.0302777077
30 ts 94.34555427 0.0292681523
31 ts 64.73930427 0.0258600320
32 ts 34.73195427 0.0241235386
33 ts 35.02727427 0.0081775652
34 mixed -36.07089573 0.0112927518
35 ts 70.12897427 0.0379346556
36 ts 19.99471427 0.0229633259
37 ts -47.09459573 0.0049750195
38 ts 77.71273427 -0.0014066722
39 mixed -67.69089573 0.0400538557
40 mixed -32.28465573 -0.1207689337
41 mixed 16.79126427 0.0523263168
42 mixed -62.89555573 -0.0955469698
43 mixed 15.11927427 0.0272606791
44 nss 49.59839427 0.0391008066
45 nss 115.08367427 0.1172053342
46 mixed 4.98434427 -0.0695270859
47 nss 30.28530427 0.0509905067
48 mixed 44.46792427 -0.0697614602
49 nss 34.61201427 -0.0403938274
50 nss 54.28720427 0.0797241984
51 ts 27.79828427 -0.0281840364
52 ts -7.14943573 -0.0263596573
53 mixed -48.45964573 -0.0099469444
54 ts -20.05117573 -0.0102488072
55 ts 32.39584427 0.0421686746
56 nss 13.09454427 -0.0021443251
57 mixed -157.32194573 0.0165855854
58 mixed -66.61223573 0.0713104841
59 nss 147.90166427 0.0856190251
60 mixed 77.48450427 0.0550141185
61 mixed 31.04187427 0.0015239027
62 mixed 78.51024427 0.0490852665
63 mixed -173.30501573 -0.1442350156
64 nss 25.27541427 0.0419450587
65 nss 13.33638427 0.0702146448
66 nss 44.18280427 -0.0072373206
67 nss -174.53808573 0.0470714728
68 nss 117.17314427 0.1020406456
69 ss -11.87163573 -0.3139367715
70 nss -57.62264573 0.2327609376
71 nss -72.46457573 0.2372056132
72 ts -4.78019573 -0.0507160671
73 ts 87.39231427 0.0070904079
74 ts 18.66020427 -0.0270214010
75 ts 54.95110427 -0.0212453810
76 ss -203.21251573 -0.3318291760
77 ss -166.27284573 -0.3636339632
78 ts -2.21004573 0.0695118236
79 ts 31.56149427 0.0598066019
80 ss -191.61832573 -0.2423843174
81 nss 66.28892427 0.0577523709
82 ts -9.88605573 -0.0495405744
83 ts 74.96780427 -0.0294710312
84 nss 32.15705427 0.2550050201
85 ts -3.85690573 -0.0518578991
86 ts -0.41635573 -0.0325285839
87 ts 0.03968427 -0.0298004673
88 ts 64.02507427 -0.0553754594
89 ts -7.76925573 -0.0327606251
90 ts 4.92271427 -0.0006402677
91 mixed 8.50073427 0.1903563445
92 mixed 107.89058427 0.1143372700
93 nss -187.35273573 -0.0660424538
94 nss -31.86566573 -0.0708189964
95 nss -18.04816573 0.0360274192
96 nss 15.77139427 -0.0555136049
97 mixed 72.83693427 0.1330965998
98 mixed 18.91322427 0.0348577054
99 mixed -32.25719573 0.0503426450
100 popcorn 57.91755427 0.0028450090
101 mixed -35.67106573 -0.0598183720
102 nss 54.85721427 0.0307984158
103 mixed -12.30189573 0.0293500647
104 ts 90.79966427 0.0210943405
105 mixed -175.59538573 -0.0509847171
106 ts 54.25050427 0.0310904390
107 mixed 20.64938427 -0.0342189803
108 ts 57.42679427 0.0223336768
109 mixed 42.63732427 0.0338396097
$layers
$layers[[1]]
geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[2]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[3]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[4]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$scales
<ggproto object: Class ScalesList, gg>
add: function
clone: function
find: function
get_scales: function
has_scale: function
input: function
n: function
non_position_scales: function
scales: list
super: <ggproto object: Class ScalesList, gg>
$mapping
Aesthetic mapping:
* `x` -> `pc`
* `y` -> `geneexp`
* `colour` -> `Subpopulation`
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$coordinates
<ggproto object: Class CoordCartesian, Coord, gg>
aspect: function
backtransform_range: function
clip: on
default: TRUE
distance: function
expand: TRUE
is_free: function
is_linear: function
labels: function
limits: list
modify_scales: function
range: function
render_axis_h: function
render_axis_v: function
render_bg: function
render_fg: function
setup_data: function
setup_layout: function
setup_panel_guides: function
setup_panel_params: function
setup_params: function
train_panel_guides: function
transform: function
super: <ggproto object: Class CoordCartesian, Coord, gg>
$facet
<ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
$plot_env
<environment: 0x1b9f7300>
$labels
$labels$y
[1] "Expression"
$labels$x
[1] "PC 3"
$labels$colour
[1] "Subpopulation"
$labels$intercept
[1] "intercept"
$labels$slope
[1] "slope"###make a plot for PC5
sigPC5 = dplyr::filter(pfdr, X5 < 0.1)
#sigPC5$gene = geneNames[sigPC5$index]
mgi = sigPC5$index[6]
alltissueresults[['LMAD26']][1,mgi]##get gene name[[1]]
[1] "GRMZM2G069762"pc5Plot = makeGenePlot(tissue='LMAD26',geneIndex=mgi, pc=5)
pc5Plot$data
Inbred State.Country NSS SS TS Pop.A Sweet.A
1 4226 Illinois 0.917 0.071 0.012 0 0
2 33-16 Indiana 0.972 0.014 0.014 0 0
3 A214NGoodman-Buckler South Africa 0.017 0.762 0.221 0 0
4 A441-5 South Africa 0.531 0.005 0.464 0 0
5 A6 Pioneer 0.03 0.003 0.967 0 0
6 Ab28A Alabama 0.776 0.002 0.222 0 0
7 B46 Iowa 0.784 0.214 0.002 0 0
8 B52 Iowa 0.985 0.012 0.003 0 0
9 B64 Iowa 0.002 0.988 0.01 0 0
10 B73 Iowa 0.001 0.999 0.001 0 0
11 B77 Iowa 0.915 0.083 0.002 0 0
12 B84 Iowa 0.003 0.996 0.001 0 0
13 Ci7Goodman-Buckler USDA 0.994 0.004 0.001 0 0
14 CI21E USDA 0.866 0.119 0.014 0 0
15 CI28AGoodman-Buckler USDA 0.749 0.004 0.247 0 0
16 CI31A USDA 0.994 0.002 0.004 0 0
17 CI64 USDA 0.986 0.012 0.002 0 0
18 CI66 USDA 0.935 0.011 0.054 0 0
19 Ci91BGoodman-Buckler USDA 0.997 0.002 0.002 0 0
20 CML10 Mexico 0.002 0.002 0.995 0 0
21 CML103 Mexico 0.005 0.003 0.992 0 0
22 CML108 Mexico 0.099 0.002 0.899 0 0
23 CML11 Mexico 0.002 0.003 0.995 0 0
24 CML154Q Mexico 0.005 0.002 0.993 0 0
25 CML157Q Mexico 0.018 0.004 0.978 0 0
26 CML158Q Mexico 0.018 0.01 0.971 0 0
27 CML218 Mexico 0.301 0.01 0.689 0 0
28 CML220 Mexico 0.053 0.059 0.887 0 0
29 CML228 Mexico 0.002 0.003 0.995 0 0
30 CML261 Mexico 0.004 0.016 0.981 0 0
31 CML277 Mexico 0.004 0.001 0.995 0 0
32 CML314 Mexico 0.002 0.003 0.995 0 0
33 CML322 Mexico 0.005 0.01 0.985 0 0
34 CML323 Mexico 0.34 0.007 0.653 0 0
35 CML331 Mexico 0.003 0.002 0.994 0 0
36 CML333 Mexico 0.029 0.013 0.959 0 0
37 CML38 Mexico 0.002 0.001 0.997 0 0
38 CML69 Mexico 0.028 0.006 0.966 0 0
39 CML77 Mexico 0.302 0.005 0.693 0 0
40 CML91 Mexico 0.218 0.015 0.767 0 0
41 D940Y South Africa 0.614 0.004 0.382 0 0
42 DE811 Delaware 0.218 0.779 0.003 0 0
43 F6 Florida 0.745 0.005 0.25 0 0
44 GA209 Georgia 0.979 0.004 0.017 0 0
45 GT112 Georgia 0.839 0.008 0.153 0 0
46 Hi27Goodman-Buckler Hawaii 0.019 0.207 0.774 0 0
47 ILLHy Illinois 0.898 0.079 0.024 0 0
48 I137TN South Africa 0.539 0.002 0.459 0 0
49 K4 Kansas 0.873 0.11 0.017 0 0
50 K64 Kansas 0.965 0.033 0.002 0 0
51 Ki11 Thailand 0.017 0.003 0.98 0 0
52 Ki14 Thailand 0.003 0.003 0.994 0 0
53 Ki21 Thailand 0.393 0.001 0.606 0 0
54 KI3 Thailand 0.002 0.001 0.997 0 0
55 Ki44 Thailand 0.018 0.001 0.981 0 0
56 Ky21 Kentucky 0.859 0.008 0.133 0 0
57 KY228 Kentucky 0.709 0.134 0.157 0 0
58 L578 Louisiana 0.297 0.007 0.696 0 0
59 M162W South Africa 0.996 0.002 0.002 0 0
60 M37W South Africa 0.404 0.012 0.584 0 0
61 Mo18W Missouri 0.307 0.011 0.683 0 0
62 Mp339 Mississippi 0.641 0.102 0.258 0 0
63 N28Ht Nebraska 0.447 0.551 0.002 0 0
64 NC222 North Carolina 0.874 0.004 0.122 0 0
65 NC230 North Carolina 0.994 0.004 0.002 0 0
66 NC232 North Carolina 0.839 0.068 0.094 0 0
67 NC236 North Carolina 0.974 0.023 0.002 0 0
68 NC238 North Carolina 0.989 0.004 0.007 0 0
69 NC250 North Carolina 0.057 0.941 0.002 0 0
70 NC258 North Carolina 0.997 0.001 0.001 0 0
71 NC262 North Carolina 0.991 0.007 0.001 0 0
72 NC296 North Carolina 0.001 0.002 0.997 0 0
73 NC300 North Carolina 0.003 0.002 0.995 0 0
74 NC302 North Carolina 0.003 0.002 0.996 0 0
75 NC304 North Carolina 0.002 0.001 0.997 0 0
76 NC306 North Carolina 0.001 0.998 0.001 0 0
77 NC314 North Carolina 0.001 0.998 0.001 0 0
78 NC318 North Carolina 0.032 0.002 0.966 0 0
79 NC320 North Carolina 0.005 0.004 0.991 0 0
80 NC326 North Carolina 0.001 0.998 0.001 0 0
81 NC33 North Carolina 0.862 0.129 0.01 0 0
82 NC336 North Carolina 0.001 0.002 0.997 0 0
83 NC338 North Carolina 0.003 0.002 0.995 0 0
84 NC342 North Carolina 0.994 0.005 0.001 0 0
85 NC346 North Carolina 0.001 0.002 0.997 0 0
86 NC348 North Carolina 0.001 0.001 0.997 0 0
87 NC350 North Carolina 0.002 0.003 0.995 0 0
88 NC352 North Carolina 0.001 0.003 0.996 0 0
89 NC354 North Carolina 0.001 0.01 0.989 0 0
90 NC356 North Carolina 0.031 0.001 0.968 0 0
91 NC360 North Carolina 0.475 0.001 0.523 0 0
92 NC366 North Carolina 0.467 0.018 0.514 0 0
93 OH7B Ohio 0.967 0.031 0.002 0 0
94 Os420 Iowa 0.983 0.016 0.001 0 0
95 Pa880 Pennsylvania 0.993 0.002 0.005 0 0
96 R4 Illinois 0.979 0.019 0.003 0 0
97 SC213R South Carolina 0.579 0.002 0.419 0 0
98 SC357 South Carolina 0.622 0.003 0.375 0 0
99 SC55 South Carolina 0.626 0.046 0.328 0 0
100 Sg1533 Indiana 0 0 0 1 0
101 T232 Tennessee 0.459 0.14 0.402 0 0
102 T234 Tennessee 0.985 0.003 0.012 0 0
103 Tx303 Texas 0.36 0.065 0.575 0 0
104 Tzi11 Nigeria 0.184 0.002 0.814 0 0
105 Tzi16 Nigeria 0.351 0.248 0.401 0 0
106 Tzi18 Nigeria 0.003 0.002 0.995 0 0
107 Tzi25 Nigeria 0.086 0.305 0.608 0 0
108 Tzi9 Nigeria 0.011 0.008 0.982 0 0
109 U267Y South Africa 0.595 0.002 0.403 0 0
Subpopulation geneexp pc
1 nss -24.61871845 -4.992286e-02
2 nss 30.12734155 1.779386e-03
3 mixed -33.29421845 4.817809e-02
4 mixed 62.22759155 -1.816871e-02
5 ts -26.04784845 -2.335540e-03
6 mixed -9.96062845 2.416049e-01
7 mixed -63.31199845 4.201860e-02
8 nss -14.92562845 3.136718e-02
9 ss 30.81365155 9.472994e-02
10 ss 4.61969155 -3.791657e-02
11 nss -8.02855845 -4.610166e-02
12 ss -36.95130845 6.262466e-02
13 nss -33.11410845 1.759157e-01
14 nss -7.33489845 -8.078255e-03
15 mixed 1.05090155 4.314510e-02
16 nss -26.38604845 -2.448266e-01
17 nss 17.04092155 -2.592133e-02
18 nss -7.83855845 1.719230e-02
19 nss -58.72786845 -6.043110e-02
20 ts -26.85965845 -2.166695e-02
21 ts 65.25100155 2.029117e-02
22 ts 15.73629155 2.068224e-03
23 ts -8.42650845 -5.157327e-03
24 ts -11.05427845 -9.475546e-03
25 ts -4.51211845 -2.770357e-02
26 ts -17.27617845 -2.594352e-04
27 mixed -35.14241845 -3.570049e-02
28 ts 74.38014155 -2.290075e-02
29 ts 50.04917155 2.709823e-05
30 ts -11.56830845 3.094735e-03
31 ts -30.11852845 5.540230e-02
32 ts -35.63074845 4.384352e-02
33 ts -71.49833845 3.569604e-03
34 mixed 64.43493155 -2.907133e-03
35 ts 3.87715155 -2.706786e-02
36 ts -35.11137845 -6.034490e-02
37 ts -18.62984845 -7.787523e-03
38 ts 0.12579155 -3.298478e-02
39 mixed 7.51030155 -3.668893e-02
40 mixed -62.57853845 1.733960e-02
41 mixed -17.81081845 -1.189559e-01
42 mixed 9.09377155 5.614906e-02
43 mixed -0.05495845 -1.514160e-02
44 nss 6.97727155 1.245288e-03
45 nss -96.94903845 4.340518e-01
46 mixed -1.59711845 1.039244e-01
47 nss 18.11096155 -3.156751e-02
48 mixed -10.43987845 3.584328e-02
49 nss -55.74589845 4.397029e-02
50 nss 86.59712155 -2.327256e-01
51 ts 43.61851155 -4.277728e-02
52 ts -18.17362845 1.022203e-02
53 mixed 29.25178155 -7.416954e-02
54 ts -15.76125845 -1.467346e-02
55 ts 40.35012155 -6.247441e-02
56 nss 16.49807155 -2.255903e-02
57 mixed -5.22636845 1.490242e-02
58 mixed 3.22736155 7.162340e-02
59 nss 12.97785155 -1.221114e-01
60 mixed 10.55433155 -1.073782e-01
61 mixed 23.77227155 -8.874649e-02
62 mixed 60.52570155 6.782570e-03
63 mixed 6.31995155 9.004463e-02
64 nss -45.40496845 3.332818e-02
65 nss -26.66296845 -3.693891e-02
66 nss -14.88551845 -1.504331e-02
67 nss -52.58171845 4.005091e-02
68 nss -83.28224845 3.934855e-01
69 ss 21.41381155 -1.104945e-01
70 nss -22.46150845 -9.890079e-02
71 nss 26.53055155 -1.570953e-01
72 ts 40.15805155 -7.005304e-03
73 ts -51.82273845 1.159979e-02
74 ts 68.15533155 -2.455551e-02
75 ts 16.56422155 -1.238946e-02
76 ss 44.77617155 -8.770302e-02
77 ss -8.65277845 -1.121956e-01
78 ts 31.64963155 -3.034954e-02
79 ts -19.29406845 -8.194578e-03
80 ss 4.80345155 -4.105367e-02
81 nss -41.13766845 6.805731e-02
82 ts 30.09747155 1.275120e-02
83 ts 17.00144155 -2.399824e-03
84 nss 27.25586155 -1.583027e-01
85 ts 60.99298155 -3.471013e-03
86 ts 35.63874155 4.714962e-03
87 ts 27.41672155 -4.280713e-03
88 ts 52.30837155 1.287760e-02
89 ts 41.21608155 7.661236e-03
90 ts 67.91174155 3.858059e-03
91 mixed 25.76442155 -1.159180e-01
92 mixed -30.15232845 -4.406376e-02
93 nss -14.15181845 5.121459e-02
94 nss -4.06535845 9.433454e-05
95 nss -27.32199845 -9.485034e-02
96 nss -25.09066845 1.907392e-02
97 mixed -89.90156845 3.906238e-01
98 mixed -3.34196845 1.308657e-01
99 mixed -9.09580845 -1.341653e-02
100 popcorn 9.77316155 -6.986636e-02
101 mixed -24.77267845 2.605717e-02
102 nss 24.25927155 1.009020e-02
103 mixed 35.13826155 7.280822e-03
104 ts 18.03633155 3.375041e-02
105 mixed -56.05305845 4.258077e-02
106 ts 40.94823155 -3.833654e-02
107 mixed 16.65235155 -1.515577e-02
108 ts 28.92271155 -3.811443e-02
109 mixed -16.19048845 -1.230561e-02
$layers
$layers[[1]]
geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[2]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[3]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$layers[[4]]
mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
$scales
<ggproto object: Class ScalesList, gg>
add: function
clone: function
find: function
get_scales: function
has_scale: function
input: function
n: function
non_position_scales: function
scales: list
super: <ggproto object: Class ScalesList, gg>
$mapping
Aesthetic mapping:
* `x` -> `pc`
* `y` -> `geneexp`
* `colour` -> `Subpopulation`
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$coordinates
<ggproto object: Class CoordCartesian, Coord, gg>
aspect: function
backtransform_range: function
clip: on
default: TRUE
distance: function
expand: TRUE
is_free: function
is_linear: function
labels: function
limits: list
modify_scales: function
range: function
render_axis_h: function
render_axis_v: function
render_bg: function
render_fg: function
setup_data: function
setup_layout: function
setup_panel_guides: function
setup_panel_params: function
setup_params: function
train_panel_guides: function
transform: function
super: <ggproto object: Class CoordCartesian, Coord, gg>
$facet
<ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
$plot_env
<environment: 0x28bf7168>
$labels
$labels$y
[1] "Expression"
$labels$x
[1] "PC 5"
$labels$colour
[1] "Subpopulation"
$labels$intercept
[1] "intercept"
$labels$slope
[1] "slope"##kernel tissue on PC 4
pvals = matrix(unlist(alltissueresults[['Kern']][4,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')}))
pfdr$index = 1:nrow(pfdr)
sigPC4 = dplyr::filter(pfdr, X4<0.1)
pc4Plot = makeGenePlot(tissue='Kern',geneIndex=sigPC4$index[1], pc=4)
pc4Plot$data
Inbred State.Country NSS SS TS Pop.A Sweet.A
1 4226 Illinois 0.917 0.071 0.012 0 0
2 4722 Indiana 0 0 0 1 0
3 33-16 Indiana 0.972 0.014 0.014 0 0
4 38-11Goodman-Buckler Indiana 0.993 0.003 0.004 0 0
5 A188 Minnesota 0.982 0.013 0.006 0 0
6 A239 Minnesota 0.963 0.035 0.002 0 0
7 A441-5 South Africa 0.531 0.005 0.464 0 0
8 A556 Minnesota 0.994 0.004 0.002 0 0
9 A6 Pioneer 0.03 0.003 0.967 0 0
10 A619 Minnesota 0.99 0.009 0.001 0 0
11 A632 Minnesota 0.004 0.993 0.004 0 0
12 A634 Minnesota 0.1 0.897 0.004 0 0
13 A635 Minnesota 0.171 0.825 0.004 0 0
14 A641 Minnesota 0.481 0.517 0.002 0 0
15 A654 Minnesota 0.915 0.083 0.002 0 0
16 A659 Minnesota 0.991 0.006 0.002 0 0
17 A661 Minnesota 0.852 0.111 0.037 0 0
18 A679 Minnesota 0.127 0.862 0.011 0 0
19 A680 Minnesota 0.004 0.993 0.002 0 0
20 A682 Minnesota 0.997 0.002 0.001 0 0
21 Ab28A Alabama 0.776 0.002 0.222 0 0
22 B10 Iowa 0.57 0.429 0.002 0 0
23 B105 Iowa 0.566 0.397 0.037 0 0
24 B14A Iowa 0.001 0.998 0.001 0 0
25 B164 Minnesota 0.756 0.233 0.011 0 0
26 B2 Missouri 0.988 0.007 0.005 0 0
27 B37 Iowa 0.002 0.997 0.001 0 0
28 B57 Iowa 0.996 0.002 0.002 0 0
29 B64 Iowa 0.002 0.988 0.01 0 0
30 B68 Iowa 0.001 0.998 0.001 0 0
31 B73 Iowa 0.001 0.999 0.001 0 0
32 B73Htrhm Iowa 0.001 0.999 0.001 0 0
33 B75 Iowa 0.993 0.005 0.002 0 0
34 B77 Iowa 0.915 0.083 0.002 0 0
35 B79 Iowa 0.693 0.305 0.002 0 0
36 B84 Iowa 0.003 0.996 0.001 0 0
37 C103 Connecticut 0.997 0.002 0.001 0 0
38 C123 Connecticut 0.952 0.047 0.002 0 0
39 C49A Minnesota 0.869 0.127 0.004 0 0
40 CH701-30 Canada - Harrow 0.993 0.004 0.003 0 0
41 CH9 Canada - Harrow 0.992 0.003 0.006 0 0
42 Ci7Goodman-Buckler USDA 0.994 0.004 0.001 0 0
43 CI187-2 USDA 0.992 0.006 0.002 0 0
44 CI21E USDA 0.866 0.119 0.014 0 0
45 CI28AGoodman-Buckler USDA 0.749 0.004 0.247 0 0
46 CI31A USDA 0.994 0.002 0.004 0 0
47 CI3A USDA 0.905 0.083 0.012 0 0
48 CI66 USDA 0.935 0.011 0.054 0 0
49 Ci91BGoodman-Buckler USDA 0.997 0.002 0.002 0 0
50 CM105 Canada-Morden 0.004 0.995 0.001 0 0
51 CM37 Canada-Morden 0.969 0.013 0.017 0 0
52 CM7 Canada-Morden 0.939 0.059 0.002 0 0
53 CML103 Mexico 0.005 0.003 0.992 0 0
54 CML11 Mexico 0.002 0.003 0.995 0 0
55 CML154Q Mexico 0.005 0.002 0.993 0 0
56 CML157Q Mexico 0.018 0.004 0.978 0 0
57 CML218 Mexico 0.301 0.01 0.689 0 0
58 CML220 Mexico 0.053 0.059 0.887 0 0
59 CML228 Mexico 0.002 0.003 0.995 0 0
60 CML314 Mexico 0.002 0.003 0.995 0 0
61 CML322 Mexico 0.005 0.01 0.985 0 0
62 CML323 Mexico 0.34 0.007 0.653 0 0
63 CML333 Mexico 0.029 0.013 0.959 0 0
64 CML38 Mexico 0.002 0.001 0.997 0 0
65 CML69 Mexico 0.028 0.006 0.966 0 0
66 CML77 Mexico 0.302 0.005 0.693 0 0
67 CML91 Mexico 0.218 0.015 0.767 0 0
68 CML92 Mexico 0.275 0.096 0.629 0 0
69 CMV3 Minnesota 0.847 0.147 0.006 0 0
70 CO106 Canada-Ottawa 0.973 0.016 0.011 0 0
71 CO125 Canada-Ontario 0.967 0.021 0.012 0 0
72 CO255 Canada-Ottawa 0.647 0.009 0.344 0 0
73 D940Y South Africa 0.614 0.004 0.382 0 0
74 DE1 Delaware B 0.98 0.018 0.002 0 0
75 DE811 Delaware 0.218 0.779 0.003 0 0
76 F2834T South Africa 0.421 0.026 0.553 0 0
77 F6 Florida 0.745 0.005 0.25 0 0
78 F7 France-Peronne 0.608 0.07 0.322 0 0
79 GA209 Georgia 0.979 0.004 0.017 0 0
80 GT112 Georgia 0.839 0.008 0.153 0 0
81 H105W Indiana 0.209 0.753 0.038 0 0
82 H49 Indiana 0.995 0.002 0.003 0 0
83 H84 Indiana 0.281 0.714 0.004 0 0
84 H91 Indiana 0.002 0.997 0.001 0 0
85 H95 Indiana 0.898 0.001 0.101 0 0
86 H99 Indiana 0.997 0.001 0.002 0 0
87 Hi27Goodman-Buckler Hawaii 0.019 0.207 0.774 0 0
88 HP301 Indiana 0 0 0 1 0
89 I137TN South Africa 0.539 0.002 0.459 0 0
90 I205 Iowa 0.767 0.23 0.003 0 0
91 I29 Iowa 0 0 0 1 0
92 IA2132Goodman-Buckler Iowa 0 0 0 0 1
93 IDS28 Iowa 0 0 0 1 0
94 IDS69 Iowa 0 0 0 1 0
95 IDS91 Iowa 0 0 0 1 0
96 Il101 Illinois 0 0 0 0 1
97 Il14H Illinois 0 0 0 0 1
98 K148 Kansas 0.886 0.087 0.027 0 0
99 K4 Kansas 0.873 0.11 0.017 0 0
100 K55 Kansas 0.98 0.01 0.01 0 0
101 K64 Kansas 0.965 0.033 0.002 0 0
102 Ki21 Thailand 0.393 0.001 0.606 0 0
103 KI3 Thailand 0.002 0.001 0.997 0 0
104 Ki43 Thailand 0.048 0.029 0.923 0 0
105 Ki44 Thailand 0.018 0.001 0.981 0 0
106 Ky21 Kentucky 0.859 0.008 0.133 0 0
107 KY226 Kentucky 0.732 0.011 0.258 0 0
108 KY228 Kentucky 0.709 0.134 0.157 0 0
109 L578 Louisiana 0.297 0.007 0.696 0 0
110 M14 Illinois C 0.849 0.012 0.139 0 0
111 M162W South Africa 0.996 0.002 0.002 0 0
112 M37W South Africa 0.404 0.012 0.584 0 0
113 MEF156-55-2 Maine? 0.854 0.007 0.139 0 0
114 Mo17 Missouri 0.998 0.001 0.001 0 0
115 Mo18W Missouri 0.307 0.011 0.683 0 0
116 MO1W Missouri 0.828 0.004 0.168 0 0
117 Mo45 Missouri 0.516 0.252 0.232 0 0
118 Mo47 Missouri 0.624 0.18 0.196 0 0
119 Mp339 Mississippi 0.641 0.102 0.258 0 0
120 MS1334 Michigan 0.884 0.002 0.114 0 0
121 MS153 Michigan 0.865 0.063 0.073 0 0
122 Ms71 Michigan 0.997 0.002 0.001 0 0
123 Mt42 Minnesota 0.765 0.004 0.231 0 0
124 N192 Nebraska 0.001 0.998 0.001 0 0
125 N28Ht Nebraska 0.447 0.551 0.002 0 0
126 NC222 North Carolina 0.874 0.004 0.122 0 0
127 NC230 North Carolina 0.994 0.004 0.002 0 0
128 NC232 North Carolina 0.839 0.068 0.094 0 0
129 NC236 North Carolina 0.974 0.023 0.002 0 0
130 NC238 North Carolina 0.989 0.004 0.007 0 0
131 NC250 North Carolina 0.057 0.941 0.002 0 0
132 NC258 North Carolina 0.997 0.001 0.001 0 0
133 NC260 North Carolina 0.992 0.005 0.003 0 0
134 NC262 North Carolina 0.991 0.007 0.001 0 0
135 NC264 North Carolina 0.018 0.015 0.967 0 0
136 NC290A North Carolina 0.984 0.015 0.001 0 0
137 NC294 North Carolina 0.001 0.998 0.001 0 0
138 NC296 North Carolina 0.001 0.002 0.997 0 0
139 NC300 North Carolina 0.003 0.002 0.995 0 0
140 NC302 North Carolina 0.003 0.002 0.996 0 0
141 NC304 North Carolina 0.002 0.001 0.997 0 0
142 NC306 North Carolina 0.001 0.998 0.001 0 0
143 NC310 North Carolina 0.001 0.999 0.001 0 0
144 NC314 North Carolina 0.001 0.998 0.001 0 0
145 NC318 North Carolina 0.032 0.002 0.966 0 0
146 NC320 North Carolina 0.005 0.004 0.991 0 0
147 NC324 North Carolina 0.001 0.999 0.001 0 0
148 NC326 North Carolina 0.001 0.998 0.001 0 0
149 NC328 North Carolina 0.001 0.998 0.001 0 0
150 NC33 North Carolina 0.862 0.129 0.01 0 0
151 NC336 North Carolina 0.001 0.002 0.997 0 0
152 NC338 North Carolina 0.003 0.002 0.995 0 0
153 NC340 North Carolina 0.004 0.009 0.987 0 0
154 NC346 North Carolina 0.001 0.002 0.997 0 0
155 NC348 North Carolina 0.001 0.001 0.997 0 0
156 NC352 North Carolina 0.001 0.003 0.996 0 0
157 NC354 North Carolina 0.001 0.01 0.989 0 0
158 NC356 North Carolina 0.031 0.001 0.968 0 0
159 NC358 North Carolina 0.143 0.001 0.856 0 0
160 NC360 North Carolina 0.475 0.001 0.523 0 0
161 NC362 North Carolina 0.54 0.002 0.458 0 0
162 NC364 North Carolina 0.507 0.002 0.492 0 0
163 NC366 North Carolina 0.467 0.018 0.514 0 0
164 NC368 North Carolina 0.003 0.996 0.001 0 0
165 ND246 North Dakota 0.76 0.004 0.236 0 0
166 Oh40B Ohio 0.996 0.002 0.002 0 0
167 Oh43 Ohio 0.998 0.001 0.001 0 0
168 Oh43E Ohio 0.997 0.002 0.001 0 0
169 Oh603 Ohio 0.669 0.002 0.329 0 0
170 OH7B Ohio 0.967 0.031 0.002 0 0
171 P39Goodman-Buckler Indiana 0 0 0 0 1
172 Pa875 Pennsylvania 0.979 0.002 0.02 0 0
173 Pa880 Pennsylvania 0.993 0.002 0.005 0 0
174 Pa91 Pennsylvania 0.997 0.001 0.002 0 0
175 R168 Illinois 0.996 0.002 0.002 0 0
176 R177 Illinois 0.875 0.122 0.002 0 0
177 R229 Illinois 0.002 0.991 0.007 0 0
178 R4 Illinois 0.979 0.019 0.003 0 0
179 SA24 Indiana 0 0 0 1 0
180 SC55 South Carolina 0.626 0.046 0.328 0 0
181 SD40 South Dakota 0.581 0.417 0.002 0 0
182 Sg1533 Indiana 0 0 0 1 0
183 Sg18 Indiana 0 0 0 1 0
184 T232 Tennessee 0.459 0.14 0.402 0 0
185 T234 Tennessee 0.985 0.003 0.012 0 0
186 T8 Tennessee 0.989 0.003 0.007 0 0
187 Tzi11 Nigeria 0.184 0.002 0.814 0 0
188 Tzi16 Nigeria 0.351 0.248 0.401 0 0
189 Tzi18 Nigeria 0.003 0.002 0.995 0 0
190 Tzi25 Nigeria 0.086 0.305 0.608 0 0
191 Tzi9 Nigeria 0.011 0.008 0.982 0 0
192 U267Y South Africa 0.595 0.002 0.403 0 0
193 VA102 Virginia 0.994 0.001 0.005 0 0
194 Va17 Virginia 0.997 0.002 0.001 0 0
195 Va22 Virginia 0.995 0.001 0.003 0 0
196 Va26 Virginia 0.983 0.005 0.011 0 0
197 Va35 Virginia 0.996 0.002 0.002 0 0
198 Va59 Virginia 0.994 0.003 0.003 0 0
199 Va85 Virginia 0.983 0.014 0.003 0 0
200 Va99 Virginia 0.84 0.158 0.002 0 0
201 VaW6 Virginia 0.724 0.02 0.256 0 0
202 W117Ht Wisconsin 0.617 0.159 0.224 0 0
203 W153R Wisconsin 0.853 0.145 0.002 0 0
204 W182B Wisconsin 0.845 0.153 0.002 0 0
205 W22 Wisconsin 0.931 0.063 0.006 0 0
206 W64A Wisconsin 0.998 0.001 0.001 0 0
207 Wf9 Indiana 0.994 0.005 0.001 0 0
Subpopulation geneexp pc
1 nss -25.02019841 -0.0112986353
2 popcorn -48.28868841 -0.0390163698
3 nss 0.31261159 0.0050132001
4 nss 30.21185159 0.0478511691
5 nss -8.79771841 -0.0174047996
6 nss -17.57814841 -0.0107516875
7 mixed -8.33127841 0.0388477767
8 nss -6.11130841 -0.0438694766
9 ts -78.81877841 0.0262343631
10 nss -0.79522841 -0.2269190009
11 ss -32.55223841 0.1672257970
12 ss 22.67124159 0.1529544214
13 ss -2.81718841 0.1499983344
14 mixed -13.40201841 0.0868156237
15 nss 11.95405159 -0.0035747083
16 nss 8.48562159 -0.0305001956
17 nss -22.04155841 -0.0062825031
18 ss 33.83101159 -0.0288094703
19 ss -44.76702841 -0.0207237728
20 nss -0.30214841 -0.0041806920
21 mixed -12.05052841 0.0268600460
22 mixed 17.32817159 0.0064827457
23 mixed -28.64313841 -0.0255734045
24 ss 24.40973159 0.1697760551
25 mixed 15.18828159 -0.0286698880
26 nss 33.96881159 -0.0148336251
27 ss -2.13721841 -0.1963054988
28 nss -18.39988841 0.0209335404
29 ss -28.26816841 0.1521852161
30 ss 12.55783159 0.1866909188
31 ss -65.33232841 -0.0257393303
32 ss -0.15534841 -0.0267446445
33 nss 32.24737159 -0.0044826423
34 nss 64.53959159 -0.0348907285
35 mixed -51.61412841 -0.0278502448
36 ss -4.18194841 0.0324217692
37 nss -17.85222841 0.0274987166
38 nss -19.44581841 0.0220138402
39 nss -23.28106841 0.0296285153
40 nss -8.21847841 0.0026082093
41 nss -47.47800841 0.0242006010
42 nss -17.81151841 -0.0199723939
43 nss 4.33305159 0.0062663903
44 nss -41.20085841 -0.0116090024
45 mixed 15.03256159 -0.0078593118
46 nss -18.35387841 -0.0220448934
47 nss 29.74429159 0.0146250722
48 nss -7.82537841 0.1089307463
49 nss 2.90495159 -0.0150913463
50 ss 12.97012159 -0.0129021537
51 nss 5.43373159 0.0266846992
52 nss 38.20666159 -0.0020123745
53 ts -13.19142841 0.0288189637
54 ts 7.34800159 0.0338769451
55 ts -67.79301841 0.0383127875
56 ts 0.16785159 0.0284118930
57 mixed 13.04737159 -0.0177687762
58 ts 4.96259159 0.0138338562
59 ts 61.56709159 0.0302663761
60 ts 10.01875159 0.0174220706
61 ts 26.85116159 0.0188789919
62 mixed -11.05871841 0.0312460845
63 ts 8.35473159 0.0214864422
64 ts 29.67054159 0.0266961730
65 ts -20.18924841 0.0125631688
66 mixed 3.44885159 0.0184505578
67 mixed 18.41474159 0.0147204755
68 mixed -12.74647841 0.0304046958
69 nss -18.19431841 -0.0281742835
70 nss -16.16657841 0.0074799580
71 nss -34.74328841 0.0161135516
72 mixed 5.44672159 -0.0373954496
73 mixed 59.62473159 0.0368093704
74 nss 11.77401159 -0.0163800745
75 mixed 23.30745159 0.1370110264
76 mixed 32.66455159 0.0374835182
77 mixed 13.26080159 0.0258252798
78 mixed -38.44140841 0.0132087418
79 nss -22.58555841 0.0377029117
80 nss -5.83602841 0.0257223418
81 mixed 19.14749159 0.1384329578
82 nss -28.11090841 0.0264968029
83 mixed -2.95841841 -0.1116748136
84 ss 2.94455159 0.1664975424
85 nss 14.40119159 -0.1441870179
86 nss 13.51154159 -0.1204628591
87 mixed 5.07405159 0.0599019889
88 popcorn 59.40973159 0.0720912730
89 mixed -56.70655841 0.0012799363
90 mixed 9.09680159 0.0282965830
91 popcorn 3.47350159 0.0523040471
92 sweet -24.38917841 0.0328374944
93 popcorn -6.96236841 0.0447377134
94 popcorn 27.49877159 0.0582991131
95 popcorn -10.79934841 0.0543863470
96 sweet 44.00459159 0.0386511070
97 sweet 8.33731159 0.0626248558
98 nss -21.16574841 0.0151276412
99 nss -18.08886841 0.0025585703
100 nss -3.46573841 0.0205309068
101 nss -24.06613841 0.0369395823
102 mixed -21.79075841 -0.0170554478
103 ts -34.58166841 0.0005885456
104 ts 14.55200159 0.0122919993
105 ts 21.48677159 0.0111827629
106 nss -28.76472841 0.0252238463
107 mixed 36.89163159 -0.0086248757
108 mixed -20.79558841 0.0330456741
109 mixed 23.49838159 0.0217403518
110 nss -38.74799841 -0.0621522468
111 nss -9.22047841 0.0399501530
112 mixed 2.67294159 0.0322626357
113 nss -1.83019841 0.0020166696
114 nss 8.47651159 0.0398811263
115 mixed 2.99949159 0.0079780896
116 nss 31.01249159 -0.0319379690
117 mixed -0.53946841 0.0323822533
118 mixed 33.92587159 0.0467371806
119 mixed 9.98939159 0.0179914445
120 nss -27.69779841 -0.0302280356
121 nss -13.68288841 0.0477164800
122 nss 58.88567159 0.0047254131
123 mixed -12.89513841 0.0146380567
124 ss -0.96919841 0.0279352630
125 mixed 8.87167159 -0.0029612960
126 nss -18.39884841 -0.0008471289
127 nss 44.68936159 0.0253490794
128 nss 55.94525159 0.0225498762
129 nss 5.47048159 0.0237145342
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stat_identity: na.rm = FALSE
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mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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mapping: intercept = ~intercept, slope = ~slope
geom_abline: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity
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find: function
get_scales: function
has_scale: function
input: function
n: function
non_position_scales: function
scales: list
super: <ggproto object: Class ScalesList, gg>
$mapping
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* `y` -> `geneexp`
* `colour` -> `Subpopulation`
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$coordinates
<ggproto object: Class CoordCartesian, Coord, gg>
aspect: function
backtransform_range: function
clip: on
default: TRUE
distance: function
expand: TRUE
is_free: function
is_linear: function
labels: function
limits: list
modify_scales: function
range: function
render_axis_h: function
render_axis_v: function
render_bg: function
render_fg: function
setup_data: function
setup_layout: function
setup_panel_guides: function
setup_panel_params: function
setup_params: function
train_panel_guides: function
transform: function
super: <ggproto object: Class CoordCartesian, Coord, gg>
$facet
<ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
$plot_env
<environment: 0x1ccd7aa0>
$labels
$labels$y
[1] "Expression"
$labels$x
[1] "PC 4"
$labels$colour
[1] "Subpopulation"
$labels$intercept
[1] "intercept"
$labels$slope
[1] "slope"postscript("../figures/heatmap.eps",height=10,width=10,paper="special",horizontal=FALSE,colormodel="cymk", onefile=FALSE)
final <- ggarrange(pl,
ggarrange(myplot, pc5Plot, ncol = 2, labels = c("B", "C"),common.legend = T, legend = "bottom"),
nrow = 2,
labels = "A"
)Warning: Removed 20 rows containing missing values (geom_text).final
dev.off()png
2
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyr_1.0.2 quaint_0.0.0.9000 ggpubr_0.2.5 magrittr_1.5
[5] reshape2_1.4.3 ggplot2_3.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 plyr_1.8.6 compiler_3.6.3 pillar_1.4.3
[5] later_1.0.0 git2r_0.26.1 highr_0.8 workflowr_1.6.1
[9] tools_3.6.3 digest_0.6.25 evaluate_0.14 lifecycle_0.2.0
[13] tibble_2.1.3 gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.5
[17] rstudioapi_0.11 yaml_2.2.1 xfun_0.12 withr_2.1.2
[21] stringr_1.4.0 dplyr_0.8.5 knitr_1.28 vctrs_0.2.4
[25] fs_1.3.2 cowplot_1.0.0 rprojroot_1.3-2 grid_3.6.3
[29] tidyselect_1.0.0 glue_1.3.2 R6_2.4.1 rmarkdown_2.1
[33] farver_2.0.3 purrr_0.3.3 whisker_0.4 ellipsis_0.3.0
[37] backports_1.1.5 scales_1.1.0 promises_1.1.0 htmltools_0.4.0
[41] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 httpuv_1.5.2
[45] labeling_0.3 stringi_1.4.6 munsell_0.5.0 crayon_1.3.4